Return and volatility spillovers between Bitcoin and other asset classes in Turkey

2019 ◽  
Vol 14 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Gulin Vardar ◽  
Berna Aydogan
Keyword(s):  
Spillover Effects ◽  
Portfolio Diversification ◽  
Volatility Spillover ◽  
Policy Makers ◽  
Content Type ◽  
Empirical Results ◽  
Volatility Spillovers ◽  
Regulatory Bodies ◽  
Asset Classes ◽  
Econometric Technique

Purpose With a substantial return and volatility characteristic of Bitcoin, which may be seen as a new category of investment assets, better understanding of the nature of return and volatility spillover can help investors and regulators in achieving the potential goal from portfolio diversification. The paper aims to discuss these issues. Design/methodology/approach This paper explores the return and volatility transmission between the Bitcoin, as the largest cryptocurrency, and other traditional asset classes, namely stock, bond and currencies from the standpoint of Turkey over the period July, 2010–June, 2018 using the newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation. Findings The empirical results reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market. Regarding the results of shock and volatility spillovers, there exists strong evidence of bidirectional cross-market shock and volatility spillover effects between Bitcoin and all other financial asset classes, except US Dollar exchange rate. Originality/value The important extention is the adoption of a newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation, proposed by Engle and Kroner (1995), which is employed for the first time specifically to examine the extent of integration in terms of volatility and return between Bitcoin and key asset classes. Second, Bitcoin has experienced a rapid growth since around a decade and a number of investors are showing interest in its potential as an integrative part of portfolio diversification. The information provided by empirical results gives empirical bases from which to address topics concerning hedging purposes and optimal portfolio allocation. It is also increasingly important to analyze the current behavior of Bitcoin in relation to other assets to provide policy makers and regulatory bodies with guidance on the role of the Bitcoin as an investment asset in Turkey. Thus, this is the first serious attempt at exploring the potential for Bitcoin to offer diversification opportunities in the context of Turkey.

Nexus between Southeast Asian stock markets, bitcoin and gold: spillover effect before and during the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yosuke Kakinuma
Keyword(s):  
Southeast Asia ◽  
Stock Markets ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Southeast Asian ◽  
Series Data ◽  
Financial Assets ◽  
Volatility Spillover ◽  
Content Type ◽  
Asset Classes

Purpose This study aims to provide empirical evidence on the return and volatility spillover effects between Southeast Asian stock markets, bitcoin and gold in the periods before and during the COVID-19 pandemic. The interdependence among different asset classes, the two leading stock markets in Southeast Asia (Singapore and Thailand), bitcoin and gold, is analyzed for diversification opportunities. Design/methodology/approach The vector autoregressive-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity model is used to capture the return and volatility spillover effects between different financial assets. The data cover the period from October 2013 to May 2021. The full period is divided into two sub-sample periods, the pre-pandemic period and the during-pandemic period, to examine whether the financial turbulence caused by COVID-19 affects the interconnectedness between the assets. Findings The stocks in Southeast Asia, bitcoin and gold become more interdependent during the pandemic. During turbulent times, the contagion effect is inevitable regardless of region and asset class. Furthermore, bitcoin does not provide protection for investors in Southeast Asia. The pricing mechanism and technology behind bitcoin are different from common stocks, yet the results indicate the co-movement of bitcoin and the Singaporean and Thai stocks during the crisis. Finally, risk-averse investors should ensure that gold constitutes a significant proportion of their portfolio, approximately 40%–55%. This strategy provides the most effective hedge against risk. Originality/value The mean return and volatility spillover is analyzed between bitcoin, gold and two preeminent stock markets in Southeast Asia. Most prior studies test the spillover effect between the same asset classes such as equities in different regions or different commodities, currencies and cryptocurrencies. Moreover, the time-series data are divided into two groups based on the structural break caused by the COVID-19 pandemic. The findings of this study offer practical implications for risk management and portfolio diversification. Diversification opportunities are becoming scarce as different financial assets witness increasing integration.

The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages

2014 ◽  
Vol 32 (6) ◽  
pp. 610-641 ◽  
Author(s):  
Kim Hiang Liow
Keyword(s):  
Real Estate ◽  
Spillover Effects ◽  
Real Estate Market ◽  
Market Volatility ◽  
Volatility Spillover ◽  
Content Type ◽  
Real Estate Markets ◽  
Volatility Spillovers ◽  
Public Property ◽  
Property Markets

Purpose – The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013. Design/methodology/approach – First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles. Findings – Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks. Research limitations/implications – Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets. Originality/value – This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

Volatility spillovers among G7, E7 stock markets and cryptocurrencies

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Berna Aydoğan ◽  
Gülin Vardar ◽  
Caner Taçoğlu
Keyword(s):  
Financial Market ◽  
Portfolio Management ◽  
Stock Markets ◽  
Negative Impact ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Content Type ◽  
Volatility Spillovers ◽  
Dynamic Relationship ◽  
The Stability

PurposeThe existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.Design/methodology/approachApplying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.FindingsInterestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.Originality/valueOverall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

scholarly journals ON PRICE CO-MOVEMENT AND VOLATILITY SPILLOVER EFFECTS IN CHINA’S HOUSING MARKETS

2017 ◽  
Vol 21 (3) ◽  
pp. 240-255 ◽  
Author(s):  
Yingliang WENG ◽  
Pu GONG
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Housing Markets ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Policy Makers ◽  
Chinese Cities ◽  
Spatial Panel ◽  
Volatility Spillovers ◽  
Spatial Panel Data

The soaring property prices in many Chinese cities have recently attracted increasing attention. This study uses the data on housing price indices from January 2005 to December 2014 in 10 large Chinese cities to analyze volatility spillover effects and to identify the determinants of price co-movement across the China’s regional housing markets. This research proposes a novel dynamic spatial panel data model that accounts for multivariate asymmetrical generalized autoregressive conditional heteroskedasticity components in disturbances to address these issues empirically. Results reveal that housing prices in cities are significantly influenced by population, income, mortgage rates, policy factors, and the national macroeconomic situation. The analysis further indicates that the housing returns of regions in China that are in close geographic and economic proximities exhibit strong co-movement and volatility spillovers. Evidence of significantly positive leverage effects in regional housing markets is also determined. This study’s findings have significant implications for academic researchers, financial experts, and policy makers.

Return and volatility spillover effects between the Turkey and the Russia stock market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Melih Kutlu ◽  
Aykut Karakaya
Keyword(s):  
Stock Exchange ◽  
Spillover Effects ◽  
Stock Exchanges ◽  
Volatility Spillover ◽  
Full Sample ◽  
Content Type ◽  
Crisis Period ◽  
Volatility Spillovers ◽  
Volatility Analysis ◽  
Economic Developments

PurposeThis study aimed to investigate return and volatility spillover between the Borsa Istanbul (BIST) and the Moscow Stock Exchange (RTS).Design/methodology/approachThis study used generalized autoregressive conditionally heteroscedasticity (GARCH) model for volatility and the Aggregate Shock (AS) model for return and volatility spillover. The data are divided into six sub-periods. Period events take place between Turkey and Russia.FindingsBIST investors considered the return and volatility of the RTS, it is observed that Moscow Stock Exchange investors considered only the return of BIST at the full sample. It is only a return spillover from BIST to RTS and neither the return nor the volatility of the RTS is spillover to BIST in the pre-crisis period. No evidence of return and volatility spillover between the BIST and the RTS in the post-crisis period. The returns and volatility spillovers between Russia and Turkey are mutual feedback in the jet crisis period.Practical implicationsEconomic developments between Turkey and Russia is growing rapidly in recent years. The return and volatility analysis between the stock exchanges of these two countries is important for investment decisions.Originality/valueThere are many studies in the literature about emerging markets. There are also Turkish and Russian stock exchanges in these studies. However, this study only examined return and volatility spillover analysis between the Turkish and Russian stock exchanges and prevents the results from being overlooked among other countries.

International stock markets Integration and dynamics of volatility spillover between the USA and South Asian markets: evidence from Global financial crisis

2020 ◽  
Vol 14 (5) ◽  
pp. 779-794
Author(s):  
Umm E. Habiba ◽  
Shen Peilong ◽  
Wenlong Zhang ◽  
Kashif Hamid
Keyword(s):  
Financial Crisis ◽  
South Asian ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Volatility Spillover ◽  
Policy Makers ◽  
Content Type ◽  
Volatility Spillovers ◽  
Emerging Stock Markets ◽  
The Usa

Purpose The purpose of this paper is to investigate the cointegration and volatility spillover dynamics between the USA and South Asian stock markets, namely, India, Pakistan and Sri Lanka. The main objective of this study is to provide the knowledge about integration of financial market and volatility spillovers before, during and after global financial crisis to investors, fund managers and policy-makers. Design/methodology/approach The Johansen and Juselius cointegration test, Granger Causality test and bivaraite EGARCH model have been applied in this study to examine integration and volatility spillovers between selected stock markets. Findings The findings show that long-term integration between the USA market and South Asian emerging stock markets. It is found that USA stock market has causal relationship with emerging stock markets in short-term. The findings of EGARCH model reveal that asymmetric volatility spillover effects significant in all selected stock markets in pre, during and post-crisis periods. Furthermore, significant volatility spillover is found from stock markets of USA to all selected South Asian markets during and post-crisis periods. However, volatility spillovers from USA to India and Sri-Lanka markets are significant, while insignificant in case of Pakistani market in pre-crisis period. Overall, we find that returns and volatility spillover effects are higher in financial crisis period as compared to non-financial crisis period. Practical implications The findings of this paper have important implications for investors, portfolio managers and policy-makers. They can take potential benefits from international portfolio diversification by considering all these facts. The understanding and knowledge of across volatility transmission help them to maximize the gains from diversification and minimize the risk. Policy-makers can develop such strategies which protect the markets of these economies from future financial crisis. Originality/value Although in finance literature numerous studies have been conducted on integration between different stock markets, most of the studies investigated the integration and volatility spillovers between developed stock markets. However, many studies also analyzed the integration among emerging stock markets in literature review but it is hard to find studies in the context of South Asian stock markets on the effect of global financial crisis on stock markets. The main contribution of this study is to investigate the stock markets integration and volatility transmission between the USA and South Asia by considering the effect of recent 2007 US subprime financial crisis.

Shock and volatility spillovers between oil and emerging seven stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muzammil Khurshid ◽  
Berna Kirkulak-Uludag
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Design Methodology ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Causality Test ◽  
Content Type ◽  
Volatility Spillovers ◽  
Oil Market ◽  
High Level

Purpose This study aims to examine the volatility spillover effects between oil and stock returns in the emerging seven economies. Design/methodology/approach In this study, the Granger causality test and vector autoregression-generalized autoregressive conditional heteroskedasticity approach to analyze the volatility spillover from 1995 to 2019 were used. The findings provide evidence of significant volatility spillover between oil and Brazil, China, India, Indonesia, Mexico, Russia and Turkey (E7) stock markets. Findings All emerging seven stock markets exhibit positive and low constant conditional correlations with oil assets. The magnitude of the correlation changes in respond to the country’s net position in the crude oil market. While a relatively high level of correlation exists between oil and the stock markets of net oil-exporting countries, a relatively low level of correlation exists between oil and the stock markets of net oil-importing countries. Originality/value The findings suggest that oil asset improves the risk-adjusted performance of a well-diversified portfolio of stocks. However, investors should invest a larger portion of their portfolios in E7 stock markets than in oil.

Evidence of leverage effects and volatility spillover among exchange rates of selected emerging and growth leading economies

2019 ◽  
Vol 11 (2) ◽  
pp. 174-192 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda ◽  
Vipul Kumar Singh ◽  
Satish Kumar
Keyword(s):  
Exchange Rates ◽  
Asset Management ◽  
Spillover Effects ◽  
International Investment ◽  
Multivariate Garch ◽  
Conditional Volatility ◽  
Substantial Impact ◽  
Content Type ◽  
Volatility Spillovers ◽  
Strongly Connected

Purpose The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies. Design/methodology/approach The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates. Findings The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods. Practical implications The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates. Originality/value This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.

scholarly journals An analysis of CEE equity market integration and their volatility spillover effects

2019 ◽  
Vol 29 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Ngo Thai Hung
Keyword(s):  
Market Integration ◽  
Design Methodology ◽  
Spillover Effects ◽  
Equity Market ◽  
Multivariate Garch ◽  
The Czech Republic ◽  
Content Type ◽  
Volatility Spillovers ◽  
Conditional Correlations ◽  
Practical Implications

Purpose The purpose of this paper is to examine the conditional correlations and spillovers of volatilities across CEE markets, namely, Hungary, Poland, the Czech Republic, Romania and Croatia, in the post-2007 financial crisis period. Design/methodology/approach The authors use five-dimensional GARCH-BEKK alongside with the CCC and DCC models. Findings The estimation results of the three models generally demonstrate that the correlations between these markets are particularly significant. Also, own-volatility spillovers are generally lower than cross-volatility spillovers for all markets. Practical implications These results recommend that investors should take caution when investing in the CEE equity markets as well as diversifying their portfolios so as to minimize risk. Originality/value Unlike the previous studies in this field, this paper is the first study using multivariate GARCH-BEKK alongside with CCC and DCC models. The study makes an outstanding contribution to the existing literature on spillover effects and conditional correlations in the CEE financial stock markets.

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Onur Polat ◽  
Eylül Kabakçı Günay
Keyword(s):  
Financial Distress ◽  
World Health ◽  
Volatility Spillover ◽  
Content Type ◽  
Frequency Bands ◽  
Market Capitalization ◽  
Time Frequency ◽  
Long Cycle ◽  
Volatility Spillovers ◽  
Before And After

Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

Sign in / Sign up

Export Citation Format

Share Document